Fuzzy reliability analysis using cellular automata for network systems

• We evaluate network reliability where arc failure rates are fuzzy numbers.
• A network topology decomposition model based on cellular automata is proposed.
• The network arc failure rates following different types of membership function.
• Simulation based model is imprecise for lower probability estimations.

This paper concentrates on the performance evaluation of networks, whose arc failure rates are not deterministic numbers, but imprecise ones. Conventional literatures analyze the network reliability assuming that the failure rates of all components in networks following the same membership function. However, most real-world networks do not abide by this regulation, especial complex ones. Therefore, in this paper, a new method is developed based on cellular automata (CA) and fuzzy logic following different types of fuzzy failure rates. The proposed method has two separate processing procedures: the computing of fuzzy numbers (CFN) and the decomposing model based on cellular automata (DB-CA). The combination of these procedures can be used to topology decomposition and the fuzzy reliability evaluation of any real network. Finally, The proposed method is improved by numerical network examples with a variety of membership functions, and the benchmark water delivery network in Shelby county is provided to estimate the feasibility and effectiveness of the proposed model.